PaRSnIP: sequence-based protein solubility prediction using gradient boosting machine.

ABSTRACT:

Motivation

Protein solubility can be a decisive factor in both research and production efficiency, and in silico sequence-based predictors that can accurately estimate solubility outcomes are highly sought.

Results

In this study, we present a novel approach termed PRotein SolubIlity Predictor (PaRSnIP), which uses a gradient boosting machine algorithm as well as an approximation of sequence and structural features of the protein of interest. Based on an independent test set, PaRSnIP outperformed other state-of-the-art sequence-based methods by more than 9% in accuracy and 0.17 in Matthew's correlation coefficient, with an overall accuracy of 74% and Matthew's correlation coefficient of 0.48. Additionally, PaRSnIP provides importance scores for all features used in training.

SUBMITTER: Rawi R 

PROVIDER: S-EPMC6031027 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature